mapclassify.HeadTailBreaks

class mapclassify.HeadTailBreaks(y)

Head/tail Breaks Map Classification for Heavy-tailed Distributions.

Parameters:

ynumpy.array

\((n,1)\), values to classify.

Attributes:

ybnumpy.array

\((n,1)\), bin IDs for observations.

binsnumpy.array

\((k,1)\), the upper bounds of each class.

kint

The number of classes.

countsnumpy.array

\((k,1)\), the number of observations falling in each class.

Notes

Head/tail Breaks is a relatively new classification method developed for data with a heavy-tailed distribution.

Implementation based on contributions by Alessandra Sozzi <alessandra.sozzi@gmail.com>.

For theoretical details see [Jia13].

Examples

>>> import mapclassify
>>> import numpy
>>> numpy.random.seed(10)
>>> cal = mapclassify.load_example()
>>> htb = mapclassify.HeadTailBreaks(cal)
>>> htb.k
3

>>> htb.counts.tolist()
[50, 7, 1]

>>> htb.bins
array([ 125.92810345,  811.26      , 4111.45      ])

>>> numpy.random.seed(123456)
>>> x = numpy.random.lognormal(3, 1, 1000)
>>> htb = mapclassify.HeadTailBreaks(x)
>>> htb.bins
array([ 32.26204423,  72.50205622, 128.07150107, 190.2899093 ,
       264.82847377, 457.88157946, 576.76046949])

>>> htb.counts.tolist()
[695, 209, 62, 22, 10, 1, 1]

__init__(y)

Methods

__init__(y)
find_bin(x)	Sort input or inputs according to the current bin estimate.
get_adcm()	Absolute deviation around class median (ADCM).
get_fmt()
get_gadf()	Goodness of absolute deviation of fit.
get_legend_classes([fmt])	Format the strings for the classes on the legend.
get_tss()	Returns sum of squares over all class means.
make(*args, **kwargs)	Configure and create a classifier that will consume data and produce classifications, given the configuration options specified by this function.
plot(gdf[, border_color, border_width, ...])	Plot a mapclassifier object.
plot_histogram([color, linecolor, ...])	Plot histogram of y with bin values superimposed
set_fmt(fmt)
table()
update([y, inplace])	Add data or change classification parameters.

Attributes

fmt